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2011

Stopping Rules for Randomized Greedy Triangulation Schemes

7 years 6 months ago
Stopping Rules for Randomized Greedy Triangulation Schemes
Many algorithms for performing inference in graphical models have complexity that is exponential in the treewidth - a parameter of the underlying graph structure. Computing the (minimal) treewidth is NPcomplete, so stochastic algorithms are sometimes used to find low width tree decompositions. A common approach for finding good decompositions is iteratively executing a greedy triangulation algorithm (e.g. minfill) with randomized tie-breaking. However, utilizing a stochastic algorithm as part of the inference task introduces a new problem - namely, deciding how long the stochastic algorithm should be allowed to execute before performing inference on the best tree decomposition found so far. We refer to this dilemma as the Stopping Problem and formalize it in terms of the total time needed to answer a probabilistic query. We propose a rule for discontinuing the search for improved decompositions and demonstrate the benefit (in terms of time saved) of applying this rule to Bayes and...
Andrew Gelfand, Kalev Kask, Rina Dechter
Added 12 Dec 2011
Updated 12 Dec 2011
Type Journal
Year 2011
Where AAAI
Authors Andrew Gelfand, Kalev Kask, Rina Dechter
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